The Virtual Operational Data Store Demystified

The Virtual Operational Data Store (VODS) can be appreciated by anyone who needs to get somewhere on time. For example, a commuter benefits from an intelligent application like Waze, to find out where traffic is backed up (say from a major accident) and to suggest alternate routes. Drivers benefit when intelligent transportations coordinate traffic lights in real time, limiting congestion. The commuter and the traffic lights receives real-time data from many different types of information systems to make on the fly decisions and to adapt to changing road conditions. Using a Virtual Operational Data Stores (VODS) helps achieve this goal.

The Virtual Operational Data Store coordinates data from daily business transactions; especially those that come from disparate computer systems. VODS make a powerful and agile tool, recognized by companies like Cisco and Denodo . VODS captured businesses attention at Portland’s February 2017 DAMA meeting through Kent Graziano’s presentation “Agile Data Warehousing: Building a Virtualized ODS”. Kent will be speaking about this at the DATAVERSITY® Enterprise Data World 2017 Conference, in Atlanta, Georgia.

What is a Virtual Operational Data Store?

Think of a Virtual Operational Data Store as a type of data intermediary to create reports on the fly from a variety of different data sources. In the “Power of Data Intermediaries,” Ravi Shankar explains that a data intermediary provides access to all of the,

“Necessary enterprise data without any worry about which systems they come from or what format they are stored. Data virtualization is a technology that is perfectly suited to playing the role of this data intermediary.”

He illustrates the use of data intermediaries by using the analogy of consumers’ abilities to select from a wide variety of products at the grocery store, without concerning themselves about where each product originated. Like groceries, business users and consumers want to have access to data on real-time transactions, although this data can come from a wide variety of places. VODS provide the means integrate data from a variety of sources.

For example, say a drugstore needs to know how much aspirin to have on hand today, for its customers. The pharmacist receives a plethora of orders from a variety of doctors’ offices, hospitals, and clinics. Each location could request a prescription through different modalities such as a customized computer system, or the Cloud, or telephone messages.

The pharmacist needs to have a report, compiled as each order comes in, showing a snap shot of drugs he or she needs to have on hand, regardless of where the data came from or how it got to the pharmacy. An Operational Data Store (ODS) supports these kinds of transactions because it focuses on,

“The operational requirements of a particular business process (for example, customer service), and on the need to allow updates and propagate those updates back to the source operational system from which the data elements were obtained.”

Virtualizing the ODS provides a way to overcome physical system boundaries by using IT abstraction technology. Virtualization technology is an,

“Agile integration platform that orchestrates data in real time or near real time from disparate data sources (whether on premise or in the Cloud) into coherent self-service data services.”

As Todd Schraml states, IT needs to accept a “suite of varying tools and platforms has become the norm. This multiplicity is true of business intelligence data analysis tools, as well as how we architect the data warehousing ecosystems”. In other words, businesses need to be flexible, realizing data may come from the Cloud, some Cloud System or a Traditional In-House Database Management Systems (DBMS). Using a VODS is one way of handling this kind of data, by serving as an umbrella for this data, processing it and integrating it in a Data Warehouse friendly package. The VODS serves as a one stop shopping Data Store allowing other applications or users to choose the needed data products and combine them.

Real-Time Operational Data

The VODS allows for one-time ad-hoc queries real time operational data, that can be used immediately. Michael Blaha provides an example through “Data Warehouses Should Stage Source Data.” Say a customer expresses concern that their account has been hacked. The business needs to provide immediate feedback to unauthorized log-ins. IT could gather a report on account logins, in the current period (say a day) through the VODS. The Virtual Operational Data Store allows for this kind of auditability, tracing the login requests back to the current source. Neil Radon backs up this claim, stating that the ODS in practice is “current valued”. The ODS provides a useful tool as data “can be used almost immediately” accounting for timing differences from the different reporting applications

Data Quality

The VODS improves Data Quality for the Data Warehouse. When the data is in the ODS data can be “scrubbed, resolved for redundancy and checked for compliance with the corresponding business rules.” The ODS puts the data in a consistent format that potentially can be downloaded to a Data Warehouse. Even better still, the ODS does this while business operations are occurring. Thus, the data consumed by the Data Warehouse is usable. For a business that has many different branches, such as a shipping company or a retail store, the VODS would transform the data it receives, from multiple sources, into a usable format, while real-time business transactions occur at any part of the day or wherever.

Drawbacks to a Virtual Operational Data Store

Using a Virtual Operational Data Store alone, without a Data Warehouse, has some significant drawbacks:

Does Not Do Analytical Processing

VODS allow for simple queries; however, it does not handle analytical processing well or the need for elaborate reports. Should a business need information on what goods to order and stock during different times of years or the best-selling products in the last month, a VODS will not be the best tool. It provides detailed information that can be overwritten as new business happens. Also, VODS center around operations, excluding information categorized organized around subjects such as customer base, products and sales. Reports needed to make business decisions and strategies need to come from a Data Warehouse

Does Not Keep a Data History

VODS work similar to short term memory, it houses information for a very brief period of time. The VODS keep information pertaining to the operational tasks at hand and current needs. Once the task has been completed, that information is gone unless it is transferred to a long-term storage device, such as a Data Warehouse.

Slower with Large Volumes of Data

VODS work best with “fast inserts and small volumes of data.” Since VODS do not do analytical processing, overwrites operational data as new, current information comes in the system, and does keep a data history, another tool, a Data Warehouse, works better with larger volumes of information. “The data in a data warehouse is typically loaded through an extraction, transformation, and loading (ETL) process from multiple data sources.” This differs from the VODS, which acts as a ticker feed, displaying real-time data. Think of the ticker feeds from the stock markets.

The Virtual Operational Data Store works great in getting a quick snapshot of business operations and to make data from multiple sources easier for a Data Warehouse to digest. But, VODS do not substitute the need for a Data Warehouse to handle data analysis, search large amounts of data quickly, and keep historical data.

About the author

Michelle Knight enjoys putting her information specialist background to use by writing technical articles on enhancing Data Quality, lending to useful information. Michelle has written articles on W3C validator for SiteProNews, SEO competitive analysis for the SLA (Special Libraries Association), Search Engine alternatives to Google, for the Business Information Alert, and Introductions on the Semantic Web, HTML 5, and Agile, Seabourne INC LLC, through AboutUs.com.
She has worked as a software tester, a researcher, and a librarian. She has over five years of experience, contracting as a quality assurance engineer at a variety of organizations including Intel, Cigna, and Umpqua Bank. During that time Michelle used HTML, XML, and SQL to verify software behavior through databases
Michelle graduated, from Simmons College, with a Masters in Library and Information with an Outstanding Information Science Student Award from the ASIST (The American Society for Information Science and Technology) and has a Bachelor of Arts in Psychology from Smith College.
Michelle has a talent for digging into data, a natural eye for detail, and an abounding curiosity about finding and using data effectively.